Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records

2014 ◽  
Vol 53 (03) ◽  
pp. 149-151 ◽  
Author(s):  
L. Schöpe ◽  
P. Knaup
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Smart Homes ◽  
Health Data ◽  
Sensor Data ◽  
Health Records ◽  
Electronic Health ◽  
Using Data

SummaryIntroduction: This editorial is part of the Focus Theme of Methods of Information in Medicine on “Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records”.Background: To increase efficiency in the health care of the future, data from innovative technology like it is used for ambient assisted living (AAL) or smart homes should be available for individual health decisions. Integrating and aggregating data from different medical devices and health records enables a comprehensive view on health data.Objectives: The objective of this paper is to present examples of the state of the art in research on information management that leads to a sustainable use and long-term storage of health data provided by innovative assistive technologies in daily living.Results: Current research deals with the perceived usefulness of sensor data, the participatory design of visual displays for presenting monitoring data, and communication architectures for integrating sensor data from home health care environments with health care providers either via a regional health record bank or via a telemedical center.Conclusions: Integrating data from AAL systems and smart homes with data from electronic patient or health records is still in an early stage. Several projects are in an advanced conceptual phase, some of them exploring feasibility with the help of prototypes. General comprehensive solutions are hardly available and should become a major issue of medical informatics research in the near future.

scholarly journals A Simulated Graphical Interface for Integrating Patient-Generated Health Data From Smartwatches With Electronic Health Records: Usability Study

10.2196/19769 ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. e19769
Author(s):  
Jordan M Alpert ◽  
Naga S Prabhakar Kota ◽  
Sanjay Ranka ◽  
Tonatiuh V Mendoza ◽  
Laurence M Solberg ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Professionals ◽  
Health Data ◽  
Graphical Interface ◽  
Usability Study ◽  
Health Records ◽  
Information Navigation ◽  
Key Stakeholders ◽  
Electronic Health

Background Wearable technology, such as smartwatches, can capture valuable patient-generated data and help inform patient care. Electronic health records provide logical and practical platforms for including such data, but it is necessary to evaluate the way the data are presented and visualized. Objective The aim of this study is to evaluate a graphical interface that displays patients’ health data from smartwatches, mimicking the integration within the environment of electronic health records. Methods A total of 12 health care professionals evaluated a simulated interface using a usability scale questionnaire, testing the clarity of the interface, colors, usefulness of information, navigation, and readability of text. Results The interface was positively received, with 14 out of the 16 questions generating a score of 5 or greater among at least 75% of participants (9/12). On an 8-point Likert scale, the highest rated features of the interface were quick turnaround times (mean score 7.1), readability of the text (mean score 6.8), and use of terminology/abbreviations (mean score 6.75). Conclusions Collaborating with health care professionals to develop and refine a graphical interface for visualizing patients’ health data from smartwatches revealed that the key elements of the interface were acceptable. The implementation of such data from smartwatches and other mobile devices within electronic health records should consider the opinions of key stakeholders as the development of this platform progresses.

E-Discovery and Health Care IT

2011 ◽  
pp. 101-114
Author(s):  
Vasupradha Vasudevan ◽  
H.R. Roa
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Information ◽  
Health Data ◽  
Health Records ◽  
Increase Risk ◽  
Electronic Health ◽  
Legal Discovery ◽  
Unexpected Outcomes

The increase in electronic health records has introduced an increase risk of litigation related to collection, storage and exchange of health information. This chapter explores the issues associated with activities involving legal discovery that can result from failure to properly manage this stored data. It offers insights into strategies that organizations can use to protect against litigation resulting from failure to properly consider and mitigate against unexpected outcomes involving legal discovery involving stored health data.

scholarly journals A Simulated Graphical Interface for Integrating Patient-Generated Health Data From Smartwatches With Electronic Health Records: Usability Study (Preprint)

2020 ◽  
Author(s):  
Jordan M Alpert ◽  
Naga S Prabhakar Kota ◽  
Sanjay Ranka ◽  
Tonatiuh V Mendoza ◽  
Laurence M Solberg ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Professionals ◽  
Health Data ◽  
Graphical Interface ◽  
Usability Study ◽  
Health Records ◽  
Information Navigation ◽  
Key Stakeholders ◽  
Electronic Health

BACKGROUND Wearable technology, such as smartwatches, can capture valuable patient-generated data and help inform patient care. Electronic health records provide logical and practical platforms for including such data, but it is necessary to evaluate the way the data are presented and visualized. OBJECTIVE The aim of this study is to evaluate a graphical interface that displays patients’ health data from smartwatches, mimicking the integration within the environment of electronic health records. METHODS A total of 12 health care professionals evaluated a simulated interface using a usability scale questionnaire, testing the clarity of the interface, colors, usefulness of information, navigation, and readability of text. RESULTS The interface was positively received, with 14 out of the 16 questions generating a score of 5 or greater among at least 75% of participants (9/12). On an 8-point Likert scale, the highest rated features of the interface were quick turnaround times (mean score 7.1), readability of the text (mean score 6.8), and use of terminology/abbreviations (mean score 6.75). CONCLUSIONS Collaborating with health care professionals to develop and refine a graphical interface for visualizing patients’ health data from smartwatches revealed that the key elements of the interface were acceptable. The implementation of such data from smartwatches and other mobile devices within electronic health records should consider the opinions of key stakeholders as the development of this platform progresses.

E-Discovery and Health Care IT

2012 ◽  
pp. 1622-1635
Author(s):  
Vasupradha Vasudevan ◽  
H.R. Rao
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Information ◽  
Health Data ◽  
Health Records ◽  
Increase Risk ◽  
Electronic Health ◽  
Legal Discovery ◽  
Unexpected Outcomes

The increase in electronic health records has introduced an increase risk of litigation related to collection, storage and exchange of health information. This chapter explores the issues associated with activities involving legal discovery that can result from failure to properly manage this stored data. It offers insights into strategies that organizations can use to protect against litigation resulting from failure to properly consider and mitigate against unexpected outcomes involving legal discovery involving stored health data.

Negotiating stigma in health care: disclosure and the role of electronic health records

2015 ◽  
Vol 24 (3) ◽  
pp. 227-241 ◽  
Author(s):  
Timothy Stablein ◽  
Joseph Lorenzo Hall ◽  
Chauna Pervis ◽  
Denise L. Anthony
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model

2021 ◽  
Author(s):  
Claire M. Campbell ◽  
Daniel R. Murphy ◽  
George E. Taffet ◽  
Anita B. Major ◽  
Christine S. Ritchie ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Conceptual Model ◽  
Health Care Quality ◽  
Quality Measures ◽  
Care Quality ◽  
Health Records ◽  
Electronic Health

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

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